What About My Design Context?: Exploring the Use of Generative AI to Support Customization of Translational Research ArtifactsDespite the wealth of knowledge in research papers, practitioners struggle to apply research results to their work due to significant research-practice gaps. This study addresses the rigor-relevance paradox, where academic rigor can undermine the practical relevance of research for designers. Specifically, we explore the potential of large language models (LLMs) to customize translational research artifacts (i.e., design cards) and improve relevance to specific designers' needs. In our preliminary study (N=15), designers defined relevance as alignment between the content of the translational artifact and their design context—including target users, modalities/domains, and design stages. Based on these findings, we implemented an LLM-powered pipeline that allows designers to customize research papers into design cards tailored to their contexts. Our evaluation (N=20) demonstrated that designers perceived customized artifacts as more relevant, actionable, valid, generative, and inspiring than those without customization—even for less topically related papers—indicating LLM-powered customization can be used to support research translation.2025DSDonghoon Shin et al.Human-LLM CollaborationPrototyping & User TestingDIS
IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded FeedbackResearch ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on broad idea generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and combinations. Our lab study (𝑁 = 20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (𝑁 = 7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher’s workflows.2025KPKevin Pu et al.University of Toronto, Department of Computer ScienceHuman-LLM CollaborationCrowdsourcing Task Design & Quality ControlUser Research Methods (Interviews, Surveys, Observation)CHI
Social-RAG: Retrieving from Group Interactions to Socially Ground AI GenerationAI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, yet risk being unhelpful or even annoying if they fail to match group preferences or behave in socially inappropriate ways. Fortunately, group spaces have a rich history of prior interactions and affordances for social feedback that can support grounding an agent's generations to a group's interests and norms. We present Social-RAG, a workflow for socially grounding agents that retrieves context from prior group interactions, selects relevant social signals, and feeds them into a language model to generate messages in a socially aligned manner. We implement this in \textsc{PaperPing}, a system for posting paper recommendations in group chat, leveraging social signals determined from formative studies with 39 researchers. From a three-month deployment in 18 channels reaching 500+ researchers, we observed PaperPing posted relevant messages in groups without disrupting their existing social practices, fostering group common ground.2025RWRuotong Wang et al.University of Washington , Paul G. Allen School of Computer Science and EngineeringHuman-LLM CollaborationCommunity Collaboration & WikipediaCHI
Qlarify: Recursively Expandable Abstracts for Dynamic Information Retrieval over Scientific PapersNavigating the vast scientific literature often starts with browsing a paper’s abstract. However, when a reader seeks additional information, not present in the abstract, they face a costly cognitive chasm during their dive into the full text. To bridge this gap, we introduce recursively expandable abstracts, a novel interaction paradigm that dynamically expands abstracts by progressively incorporating additional information from the papers’ full text. This lightweight interaction allows scholars to specify their information needs by quickly brushing over the abstract or selecting AI-suggested expandable entities. Relevant information is synthesized using a retrieval-augmented generation approach, presented as a fluid, threaded expansion of the abstract, and made efficiently verifiable via attribution to relevant source-passages in the paper. Through a series of user studies, we demonstrate the utility of recursively expandable abstracts and identify future opportunities to support low-effort and just-in-time exploration of long-form information contexts through LLM-powered interactions.2024RFRaymond Fok et al.Human-LLM CollaborationInteractive Data VisualizationUIST
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Professional WritersThe development of large language models (LLMs) capable of following instructions and engaging in conversational interactions has led to increased interest in their use across various support tools. We investigate the effectiveness of contemporary LLMs in assisting professional writers via an empirical user study (n=30). The design of our collaborative writing interface is grounded in the cognitive process model of writing. This allows writers to obtain model help in each of the three non-linear cognitive activities in the writing process: planning, translating and reviewing. Participants write short fiction/non-fiction with model help and are subsequently asked to submit a post-completion survey to provide qualitative feedback on the potential and pitfalls of LLMs as writing collaborators. Upon analyzing the writer-LLM interactions, we find that while seeking help across all three types of cognitive activities, writers find LLMs more helpful in translation and reviewing. Our findings from analyzing both the interactions and the survey responses highlight future research directions in creative writing assistance using LLMs.2024TCTuhin Chakrabarty et al.Human-LLM CollaborationAI-Assisted Creative WritingC&C
From Paper to Card: Transforming Design Implications with Generative AICommunicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.2024DSDonghoon Shin et al.University of WashingtonGenerative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsParticipatory DesignCHI
Mitigating Barriers to Public Social Interaction with Meronymous CommunicationIn communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask "newbie" questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.2024NSNouran Soliman et al.Massachusetts Institute of TechnologySocial Platform Design & User BehaviorOnline Identity & Self-PresentationParticipatory DesignCHI
PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected PapersWith the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users’ research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers.2024YLYoonjoo Lee et al.KAISTHuman-LLM CollaborationMental Health Apps & Online Support CommunitiesCHI
Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audienceLanguage models (LMs) show promise as tools for communicating science to the general public by simplifying and summarizing complex language. Because models can be prompted to generate text for a specific audience (e.g., college-educated adults), LMs might be used to create multiple versions of plain language summaries for people with different familiarities of scientific topics. However, it is not clear what the benefits and pitfalls of adaptive plain language are. When is simplifying necessary, what are the costs in doing so, and do these costs differ for readers with different background knowledge? Through three within-subjects studies in which we surface summaries for different envisioned audiences to participants of different backgrounds, we found that while simpler text led to the best reading experience for readers with little to no familiarity in a topic, high familiarity readers tended to ignore certain details in overly plain summaries (e.g., study limitations). Our work provides methods and guidance on ways of adapting plain language summaries beyond the single "general" audience.2024TATal August et al.Allen Institute for AIHuman-LLM CollaborationExplainable AI (XAI)AI Ethics, Fairness & AccountabilityCHI
Counterspeakers’ Perspectives: Unveiling Barriers and AI Needs in the Fight against Online HateCounterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose.2024JMJimin Mun et al.Carnegie Mellon UniversityHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityCHI
A Design Space for Intelligent and Interactive Writing AssistantsIn our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.2024MLMina Lee et al.Microsoft ResearchHuman-LLM CollaborationAI-Assisted Creative WritingCreative Collaboration & Feedback SystemsCHI
FigurA11y: AI Assistance for Writing Scientific Alt TextHigh-quality alt text is crucial for making scientific figures accessible to blind and low-vision readers. Crafting complete, accurate alt text is challenging even for domain experts, as published figures often depict complex visual information and readers have varied informational needs. These challenges, along with high diversity in figure types and domain-specific details, also limit the usefulness of fully automated approaches. Consequently, the prevalence of high-quality alt text is very low in scientific papers today. We investigate whether and how human-AI collaborative editing systems can help address the difficulty of writing high-quality alt text for complex scientific figures. We present FigurA11y, an interactive system that generates draft alt text and provides suggestions for author revisions using a pipeline driven by extracted figure and paper metadata. We test two versions, motivated by prior work on visual accessibility and writing support. The base Draft+Revise version provides authors with an automatically generated draft description to revise, along with extracted figure metadata and figure-specific alt text guidelines to support the revision process. The full Interactive Assistance version further adds contextualized suggestions: text snippets to iteratively produce descriptions, and hypothetical user questions with possible answers to reveal potential ambiguities and resolutions. In a study of authors (N=14), we found the system assisted them in efficiently producing descriptive alt text. Generated drafts and interface elements enabled authors to quickly initiate and edit detailed descriptions. Additionally, interactive suggestions from the full system prompted more iteration and highlighted aspects for authors to consider, resulting in greater deviation from the drafts without increased average cognitive load or manual effort.2024NSNikhil Singh et al.Explainable AI (XAI)Visual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)IUI
Papeos: Augmenting Research Papers with Talk VideosResearch consumption has been traditionally limited to the reading of academic papers—a static, dense, and formally written format. Alternatively, pre-recorded conference presentation videos, which are more dynamic, concise, and colloquial, have recently become more widely available but potentially under-utilized. In this work, we explore the design space and benefits for combining academic papers and talk videos to leverage their complementary nature to provide a rich and fluid research consumption experience. Based on formative and co-design studies, we present Papeos, a novel reading and authoring interface that allow authors to augment their papers by segmenting and localizing talk videos alongside relevant paper passages with automatically generated suggestions. With Papeos, readers can visually skim a paper through clip thumbnails, and fluidly switch between consuming dense text in the paper or visual summaries in the video. In a comparative lab study (n=16), Papeos reduced mental load, scaffolded navigation, and facilitated more comprehensive reading of papers.2023TKTae Soo Kim et al.Generative AI (Text, Image, Music, Video)Data StorytellingUIST
Synergi: A Mixed-Initiative System for Scholarly Synthesis and SensemakingEfficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.2023HKHyeonsu B Kang et al.Human-LLM CollaborationInteractive Data VisualizationKnowledge Management & Team AwarenessUIST
ComLittee: Literature Discovery with Personal Elected Author CommitteesIn order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users’ evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee’s author-centric interaction supports curating research threads from individual authors, finding new authors and papers using combined signals from a paper recommender and the curated authors’ authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to a paper-centric discovery system with author-highlighting, we demonstrate how ComLittee improves author and paper discovery.2023HKHyeonsu B Kang et al.Carnegie Mellon UniversityRecommender System UXCrowdsourcing Task Design & Quality ControlKnowledge Management & Team AwarenessCHI
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical ContextWhen reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during literature reviews. This paper introduces CiteSee, a paper reading tool that leverages a user's publishing, reading, and saving activities to provide personalized visual augmentations and context around citations. First, CiteSee connects the current paper to familiar contexts by surfacing known citations a user had cited or opened. Second, CiteSee helps users prioritize their exploration by highlighting relevant but unknown citations based on saving and reading history. We conducted a lab study that suggests CiteSee is significantly more effective for paper discovery than three baselines. A field deployment study shows CiteSee helps participants keep track of their explorations and leads to better situational awareness and increased paper discovery via inline citation when conducting real-world literature reviews.2023JCJoseph Chee Chang et al.Allen Institute for AIInteractive Data VisualizationPrototyping & User TestingCHI
Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated CommunicationTraditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer natural-sounding suggestions, offering more advanced assistance opportunities. This study explores the trade-offs between sentence- vs. message-level suggestions for AI-mediated communication. We recruited 120 participants to act as staffers from legislators' offices who often need to respond to large volumes of constituent concerns. Participants were asked to reply to emails with different types of assistance. The results show that participants receiving message-level suggestions responded faster and were more satisfied with the experience, as they mainly edited the suggested drafts. In addition, the texts they wrote were evaluated as more helpful by others. In comparison, participants receiving sentence-level assistance retained a higher sense of agency, but took longer for the task as they needed to plan the flow of their responses and decide when to use suggestions. Our findings have implications for designing task-appropriate communication assistance systems.2023LFLiye Fu et al.Cornell UniversityHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Relatedly: Scaffolding Literature Reviews with Existing Related Work SectionsScholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers’ related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list.2023SPSrishti Palani et al.University of California, Allen Institute for AIExplainable AI (XAI)Prototyping & User TestingCHI
Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific PapersReading academic papers is a fundamental part of higher education and research, but navigating these information-dense texts can be challenging. In particular, low-vision readers using magnification encounter additional barriers to quickly skimming and visually locating information. In this work, we explored the design of interfaces to enable readers to: 1) navigate papers more easily, and 2) input the required navigation hooks that AI cannot currently automate. To explore this design space, we ran two exploratory studies. The first focused on current practices of low-vision paper readers, the challenges they encounter, and the interfaces they desire. During this study, low-vision participants were interviewed, and tried out four new paper navigation prototypes. Results from this study grounded the design of our end-to-end system prototype Ocean, which provides an accessible front-end for low-vision readers, and enables all readers to contribute to the backend by leaving traces of their reading paths for others to leverage. Our second study used this exploratory interface in a field study with groups of low-vision and sighted readers to probe the user experience of reading and creating traces. Our findings suggest that it may be possible for readers of all abilities to organically leave traces in papers, and that these traces can be used to facilitate navigation tasks, in particular for low-vision readers. Based on our findings, we present design considerations for creating future paper-reading tools that improve access, and organically source the required data from readers.2022SPSoya Park et al.Visual Impairments; Visual ImpairmentsCSCW
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge GraphsThe vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses, that improves exploratory search over a KG by obtaining new leverage from the existing preference models that KG-based systems maintain for recommending content. The approach is based on a simple but powerful observation: in a KG, preference models can be re-targeted to recommend not only entities of a single base entity type (e.g., papers in the scientific literature KG, products in an e-commerce KG), but also all other types (e.g., authors, conferences, institutions; sellers, buyers). We implement our technique in a novel system, FeedLens, which is built over Semantic Scholar, a production system for navigating the scientific literature KG. FeedLens reuses the existing preference models on Semantic Scholar---people's curated research feeds---as lenses for exploratory search. Semantic Scholar users can curate multiple feeds/lenses for different topics of interest, e.g., one for human-centered AI and another for document embeddings. Although these lenses are defined in terms of papers, FeedLens re-purposes them to also guide search over authors, institutions, venues, etc. Our system design is based on feedback from intended users via two pilot surveys (n=17 and n=13, respectively). We compare FeedLens and Semantic Scholar via a third (within-subjects) user study (n=15) and find that FeedLens increases user engagement while reducing the cognitive effort required to complete a short literature review task. Our qualitative results also highlight people's preference for this more effective exploratory search experience enabled by FeedLens.2022HKHarmanpreet Kaur et al.Recommender System UXInteractive Data VisualizationVisualization Perception & CognitionUIST